Add check for input shape (qubvel-org#549)

Author: qubvel

segmentation_models_pytorch/base/model.py

@@ -9,8 +9,23 @@ def initialize(self):
         if self.classification_head is not None:
             init.initialize_head(self.classification_head)
 
+    def check_input_shape(self, x):
+
+        h, w = x.shape[-2:]
+        output_stride = self.encoder.output_stride
+        if h % output_stride != 0 or w % output_stride != 0:
+            new_h = (h // output_stride + 1) * output_stride if h % output_stride != 0 else h
+            new_w = (w // output_stride + 1) * output_stride if w % output_stride != 0 else w
+            raise RuntimeError(
+                f"Wrong input shape height={h}, width={w}. Expected image height and width "
+                f"divisible by {output_stride}. Consider pad your images to shape ({new_h}, {new_w})."
+            )
+
     def forward(self, x):
         """Sequentially pass `x` trough model`s encoder, decoder and heads"""
+
+        self.check_input_shape(x)
+
         features = self.encoder(x)
         decoder_output = self.decoder(*features)
 

segmentation_models_pytorch/encoders/_base.py

@@ -12,11 +12,17 @@ class EncoderMixin:
     - patching first convolution for arbitrary input channels
     """
 
+    _output_stride = 32
+
     @property
     def out_channels(self):
         """Return channels dimensions for each tensor of forward output of encoder"""
         return self._out_channels[: self._depth + 1]
 
+    @property
+    def output_stride(self):
+        return min(self._output_stride, 2 ** self._depth)
+
     def set_in_channels(self, in_channels, pretrained=True):
         """Change first convolution channels"""
         if in_channels == 3:
@@ -49,6 +55,8 @@ def make_dilated(self, output_stride):
         else:
             raise ValueError("Output stride should be 16 or 8, got {}.".format(output_stride))
 
+        self._output_stride = output_stride
+
         stages = self.get_stages()
         for stage_indx, dilation_rate in zip(stage_list, dilation_list):
             utils.replace_strides_with_dilation(

segmentation_models_pytorch/encoders/timm_universal.py

@@ -24,6 +24,7 @@ def __init__(self, name, pretrained=True, in_channels=3, depth=5, output_stride=
             in_channels,
         ] + self.model.feature_info.channels()
         self._depth = depth
+        self._output_stride = output_stride
 
     def forward(self, x):
         features = self.model(x)
@@ -35,3 +36,7 @@ def forward(self, x):
     @property
     def out_channels(self):
         return self._out_channels
+
+    @property
+    def output_stride(self):
+        return min(self._output_stride, 2 ** self._depth)